{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T14:03:43Z","timestamp":1787061823346,"version":"build-2736575974"},"reference-count":55,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T00:00:00Z","timestamp":1779753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to explore the potential characteristics and associated factors of digital health literacy among patients with coronary heart disease, and to guide targeted intervention measures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study used convenience sampling to recruit 341 patients with coronary heart disease from a tertiary hospital in the Guangxi region from March to May 2025. Data were collected using a general information questionnaire, the Chinese version of the Digital Health Literacy Scale, the Health Belief Model Scale, and the Social Support Rating Scale. Latent profile analysis (LPA) was conducted using Mplus 8.3 to identify latent profiles of digital health literacy. Subsequently, univariate and multinomial logistic regression analyses were performed using SPSS 27.0 to explore factors associated with the identified profiles.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among the 341 patients with coronary heart disease, the mean age was 63.38 years (SD = 9.96). The mean digital health literacy score was 19.27 (SD = 9.27), indicating a relatively low level. LPA identified three distinct profiles: C1-Low Literacy, Passive Reception Type (48.1%); C2-Moderate Literacy, Application but Poor Appraisal Type (31.7%); and C3-High Literacy, Autonomous Decision-Making Type (20.2%). Multinomial logistic regression analysis revealed that older age was associated with lower digital health literacy, while higher education level, urban residence, greater social support, and stronger health beliefs were associated with higher digital health literacy profiles (all\n                      <jats:italic>p<\/jats:italic>\n                      &amp;lt; 0.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Digital health literacy among patients with coronary heart disease was generally low and exhibited significant heterogeneity across profiles. Healthcare providers should develop tailored interventions based on the characteristics of different subgroups to improve digital health literacy in this population.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1844333","type":"journal-article","created":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T06:03:45Z","timestamp":1779775425000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Latent profile and associated factors of digital health literacy among patients with coronary heart disease: a cross-sectional study"],"prefix":"10.3389","volume":"14","author":[{"given":"Lan","family":"Luo","sequence":"first","affiliation":[{"name":"China-ASEAN Geriatric Cardiology Division, First Affiliated Hospital of Guangxi Medical University","place":["Nanning, Guangxi, 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